7.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Mayo Clinic in ArizonaUS31 papers
- WinnMedUS22 papers
- Mayo ClinicUS20 papers
- Massachusetts General HospitalUS9 papers
- Emory UniversityUS7 papers
- The University of Texas Health Science Center at HoustonUS7 papers
- Yale UniversityUS7 papers
- Harvard UniversityUS6 papers
- University of PennsylvaniaUS6 papers
- Arizona State UniversityUS5 papers
- Cornell UniversityUS4 papers
- Jacksonville CollegeUS4 papers
12 papers · 1 filter
Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk
Theodorus Dapamede, Aisha Urooj, Vedant Joshi +15
Women are underdiagnosed and undertreated for cardiovascular disease. Automatic quantification of breast arterial calcification on screening mammography can identify women at risk…
A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images
Peilong Wang, Timothy L. Kline, Andy D. Missert +6
Automated segmentation tools often encounter accuracy and adaptability issues when applied to images of different pathology. The purpose of this study is to explore the feasibility…
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
Dominic LaBella, Ujjwal Baid, Omaditya Khanna +119
We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in…
Analysis and Validation of Image Search Engines in Histopathology
Isaiah Lahr, Saghir Alfasly, Peyman Nejat +16
Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and d…
MedYOLO: A Medical Image Object Detection Framework
Joseph Sobek, Jose R. Medina Inojosa, Betsy J. Medina Inojosa +4
Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to…
Immunohistochemistry Biomarkers-Guided Image Search for Histopathology
Abubakr Shafique, Morteza Babaie, Ricardo Gonzalez +1
Medical practitioners use a number of diagnostic tests to make a reliable diagnosis. Traditionally, Haematoxylin and Eosin (H&E) stained glass slides have been used for cancer diag…
Composite Biomarker Image for Advanced Visualization in Histopathology
Abubakr Shafique, Morteza Babaie, Ricardo Gonzalez +3
Immunohistochemistry (IHC) biomarkers are essential tools for reliable cancer diagnosis and subtyping. It requires cross-staining comparison among Whole Slide Images (WSIs) of IHCs…
CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging
Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Michael B. Gotway +1
Recently, self-supervised instance discrimination methods have achieved significant success in learning visual representations from unlabeled photographic images. However, given th…
Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning
Taimur Hassan, Bilal Hassan, Muhammad Usman Akram +3
Retinopathy represents a group of retinal diseases that, if not treated timely, can cause severe visual impairments or even blindness. Many researchers have developed autonomous sy…
Deep-LIBRA: Artificial intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment
Omid Haji Maghsoudi, Aimilia Gastounioti, Christopher Scott +7
Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates report…
Federated Learning for Breast Density Classification: A Real-World Implementation
Holger R. Roth, Ken Chang, Praveer Singh +40
Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical ima…
Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet
Sara Ranjbar, Kyle W. Singleton, Lee Curtin +5
Whole brain extraction, also known as skull stripping, is a process in neuroimaging in which non-brain tissue such as skull, eyeballs, skin, etc. are removed from neuroimages. Skul…